Distributed predefined-time algorithms for optimal solution seeking in multi-agent systems subject to input disturbances
This paper presents a novel incremental consensus-based algorithm for solving a class of distributed optimization problems in multi-agent systems, considering input disturbances, equality constraints, and box constraints. Traditional methods rely on average consensus to maintain the satisfaction of...
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| Vydané v: | Automatica (Oxford) Ročník 174; s. 112139 |
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| Hlavní autori: | , , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Elsevier Ltd
01.04.2025
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| ISSN: | 0005-1098 |
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| Abstract | This paper presents a novel incremental consensus-based algorithm for solving a class of distributed optimization problems in multi-agent systems, considering input disturbances, equality constraints, and box constraints. Traditional methods rely on average consensus to maintain the satisfaction of equality constraints throughout the entire evolution process. However, in practical applications, input disturbances can disrupt these equality constraints, rendering traditional methods ineffective. To address this challenge, the proposed algorithm combines integration sliding mode control technology with the observer methodology, creating a unified framework capable of handling input disturbances and preventing the system state from deviating beyond the solution space defined by the equality and box constraints. Moreover, the proposed algorithm offers the advantage of ensuring that all agents reach the optimal solution within a predefined time frame. This settling time can be directly adjusted by modifying one or more parameters. Finally, several numerical examples are validated to demonstrate the effectiveness and performance of the proposed algorithm. |
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| AbstractList | This paper presents a novel incremental consensus-based algorithm for solving a class of distributed optimization problems in multi-agent systems, considering input disturbances, equality constraints, and box constraints. Traditional methods rely on average consensus to maintain the satisfaction of equality constraints throughout the entire evolution process. However, in practical applications, input disturbances can disrupt these equality constraints, rendering traditional methods ineffective. To address this challenge, the proposed algorithm combines integration sliding mode control technology with the observer methodology, creating a unified framework capable of handling input disturbances and preventing the system state from deviating beyond the solution space defined by the equality and box constraints. Moreover, the proposed algorithm offers the advantage of ensuring that all agents reach the optimal solution within a predefined time frame. This settling time can be directly adjusted by modifying one or more parameters. Finally, several numerical examples are validated to demonstrate the effectiveness and performance of the proposed algorithm. |
| ArticleNumber | 112139 |
| Author | Ge, Ming-Feng He, Dingxin Chi, Ming Yang, Tao Xu, Jing-Zhe Liu, Zhi-Wei |
| Author_xml | – sequence: 1 givenname: Jing-Zhe surname: Xu fullname: Xu, Jing-Zhe email: jzxu@hust.edu.cn organization: School of Artificial Intelligence and Automation and the Key Laboratory of Image Processing and Intelligent Control, Ministry of Education, Huazhong University of Science and Technology, Wuhan, 430074, PR China – sequence: 2 givenname: Zhi-Wei surname: Liu fullname: Liu, Zhi-Wei email: zwliu@hust.edu.cn organization: School of Artificial Intelligence and Automation and the Key Laboratory of Image Processing and Intelligent Control, Ministry of Education, Huazhong University of Science and Technology, Wuhan, 430074, PR China – sequence: 3 givenname: Ming-Feng surname: Ge fullname: Ge, Ming-Feng email: gemf@cug.edu.cn organization: School of Mechanical Engineering and Electronic Information, China University of Geosciences, Wuhan, 430074, PR China – sequence: 4 givenname: Tao surname: Yang fullname: Yang, Tao email: yangtao@mail.neu.edu.cn organization: State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang, 110819, PR China – sequence: 5 givenname: Ming surname: Chi fullname: Chi, Ming email: chiming@hust.edu.cn organization: School of Artificial Intelligence and Automation and the Key Laboratory of Image Processing and Intelligent Control, Ministry of Education, Huazhong University of Science and Technology, Wuhan, 430074, PR China – sequence: 6 givenname: Dingxin surname: He fullname: He, Dingxin email: hedingxin@hust.edu.cn organization: School of Artificial Intelligence and Automation and the Key Laboratory of Image Processing and Intelligent Control, Ministry of Education, Huazhong University of Science and Technology, Wuhan, 430074, PR China |
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| Keywords | Distributed optimization Disturbance rejection Predefined-time convergence Multi-agent systems |
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| SubjectTerms | Distributed optimization Disturbance rejection Multi-agent systems Predefined-time convergence |
| Title | Distributed predefined-time algorithms for optimal solution seeking in multi-agent systems subject to input disturbances |
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